SoroushVahidi/scidocs huggingface dataset:
How effective is the IEEE 802.11 RTS/CTS handshake in ad hoc networks
(2002)

IEEE 802.11 MAC mainly relies on two techniques to combat interference: physical carrier sensing and RTS/CTS handshake (also known as “virtual carrier sensing”). Ideally, the RTS/CTS handshake can eliminate most interference. However, the effectiveness of RTS/CTS handshake is based on the assumption that hidden nodes are within transmission range of receivers. In this paper, we prove using analytic models that in ad hoc networks, such an assumption cannot hold due to the fact that power needed for interrupting a packet reception is much lower than that of delivering a packet successfully. Thus, the “virtual carrier sensing” implemented by RTS/CTS handshake cannot prevent all interference as we expect in theory. Physical carrier sensing can complement this in some degree. However, since interference happens at receivers, while physical carrier sensing is detecting transmitters (the same problem causing the hidden terminal situation), physical carrier sensing cannot help much, unless a very large carrier sensing range is adopted, which is limited by the antenna sensitivity. In this paper, we investigate how effective is the RTS/CTS handshake in terms of reducing interference. We show that in some situations, the interference range is much larger than transmission range, where RTS/CTS cannot function well. Then, a simple MAC layer scheme is proposed to solve this problem. Simulation results verify that our scheme can help IEEE 802.11 resolve most interference caused by large interference range.
Learning recursion: Multiple nested and crossed dependencies
(2011)

Language acquisition in both natural and artificial language learning settings crucially depends on extracting information from ordered sequences. A shared sequence learning mechanism is thus assumed to underlie both natural and artificial language learning. A growing body of empirical evidence is consistent with this hypothesis. By means of artificial language learning experiments, we may therefore gain more insight in this shared mechanism. In this paper, we review empirical evidence from artificial language learning and computational modeling studies, as well as natural language data, and suggest that there are two key factors that help determine processing complexity in sequence learning, and thus in natural language processing. We propose that the specific ordering of non-adjacent dependencies (i.e. nested or crossed), as well as the number of non-adjacent dependencies to be resolved simultaneously (i.e. two or three) are important factors in gaining more insight into the boundaries of human sequence learning; and thus, also in natural language processing. The implications for theories of linguistic competence are discussed.
Validity and Utility of Alternative Predictors of Job Performance
(2005)

Meta-analysis of the cumulative research on various predictors of job performance shows that for entry-level jobs there is no predictor with validity equal to that of ability, which has a mean validity of .53. For selection on the basis of current job performance, the work sample test, with mean validity of .54, is slightly better. For federal entry-level jobs, substitution of an alternative predictor would cost from $3.12 billion (job tryout) to $15.89 billion per year (age). Hiring on ability has a utility of $15.61 billion per year, but affects minority groups adversely. Hiring on ability by quotas would decrease this utility by 5%. A third strategy—using a low cutoff score—would decrease utility by 83%. Using other predictors in conjunction with ability tests might improve validity and reduce adverse impact, but there is as yet no data base for studying this possibility.
Enhancing Cross-Device Interaction Scripting with Interactive Illustrations
(2016)

Cross-device interactions involve input and output on multiple computing devices. Implementing and reasoning about interactions that cover multiple devices with a diversity of form factors and capabilities can be complex. To assist developers in programming cross-device interactions, we created DemoScript, a technique that automatically analyzes a cross-device interaction program while it is being written. DemoScript visually illustrates the step-by-step execution of a selected portion or the entire program with a novel, automatically generated cross-device storyboard visualization. In addition to helping developers understand the behavior of the program, DemoScript also allows developers to revise their program by interactively manipulating the cross-device storyboard. We evaluated DemoScript with 8 professional programmers and found that DemoScript significantly improved development efficiency by helping developers interpret and manage cross-device interaction; it also encourages testing to think through the script in a development process.
An alginate-antacid formulation (Gaviscon Double Action Liquid) can eliminate or displace the postprandial 'acid pocket' in symptomatic GERD patients.
(2011)

BACKGROUND
Recently, an 'acid pocket' has been described in the proximal stomach, particularly evident postprandially in GERD patients, when heartburn is common. By creating a low density gel 'raft' that floats on top of gastric contents, alginate-antacid formulations may neutralise the 'acid pocket'.


AIM
To assess the ability of a commercial high-concentration alginate-antacid formulation to neutralize and/or displace the acid pocket in GERD patients.


METHODS
The 'acid pocket' was studied in ten symptomatic GERD patients. Measurements were made using concurrent stepwise pH pull-throughs, high resolution manometry and fluoroscopy in a semi-recumbent posture. Each subject was studied in three conditions: fasted, 20 min after consuming a high-fat meal and 20 min later after a 20 mL oral dose of an alginate-antacid formulation (Gaviscon Double Action Liquid, Reckitt Benckiser Healthcare, Hull, UK). The relative position of pH transition points (pH >4) to the EGJ high-pressure zone was analysed.


RESULTS
Most patients (8/10) exhibited an acidified segment extending from the proximal stomach into the EGJ when fasted that persisted postprandially. Gaviscon neutralised the acidified segment in six of the eight subjects shifting the pH transition point significantly away from the EGJ. The length and pressure of the EGJ high-pressure zone were minimally affected.


CONCLUSIONS
Gaviscon can eliminate or displace the 'acid pocket' in GERD patients. Considering that EGJ length was unchanged throughout, this effect was likely attributable to the alginate 'raft' displacing gastric contents away from the EGJ. These findings suggest the alginate-antacid formulation to be an appropriately targeted postprandial GERD therapy.
Model compression via distillation and quantization
(2018)

Deep neural networks (DNNs) continue to make significant advances, solving tasks from image classification to translation or reinforcement learning. One aspect of the field receiving considerable attention is efficiently executing deep models in resource-constrained environments, such as mobile or embedded devices. This paper focuses on this problem, and proposes two new compression methods, which jointly leverage weight quantization and distillation of larger networks, called “teachers,” into compressed “student” networks. The first method we propose is called quantized distillation and leverages distillation during the training process, by incorporating distillation loss, expressed with respect to the teacher network, into the training of a smaller student network whose weights are quantized to a limited set of levels. The second method, differentiable quantization, optimizes the location of quantization points through stochastic gradient descent, to better fit the behavior of the teacher model. We validate both methods through experiments on convolutional and recurrent architectures. We show that quantized shallow students can reach similar accuracy levels to state-of-the-art full-precision teacher models, while providing up to order of magnitude compression, and inference speedup that is almost linear in the depth reduction. In sum, our results enable DNNs for resource-constrained environments to leverage architecture and accuracy advances developed on more powerful devices.
Expectancy-Value Theory of Achievement Motivation.
(2000)

We discuss the expectancy-value theory of motivation, focusing on an expectancy-value model developed and researched by Eccles, Wigfield, and their colleagues. Definitions of crucial constructs in the model, including ability beliefs, expectancies for success, and the components of subjective task values, are provided. These definitions are compared to those of related constructs, including self-efficacy, intrinsic and extrinsic motivation, and interest. Research is reviewed dealing with two issues: (1) change in children's and adolescents' ability beliefs, expectancies for success, and subjective values, and (2) relations of children's and adolescents' ability-expectancy beliefs and subjective task values to their performance and choice of activities. Copyright 2000 Academic Press.
Smart street light system looking like usual street lights based on sensor networks
(2013)

Currently, in the whole world, enormous electric energy is consumed by the street lights, which are automatically turn on when it becomes dark and automatically turn off when it becomes bright. This is the huge waste of energy in the whole world and should be changed. This paper discusses a smart street light system, whose concept is proposed by Fujii et al. The main aim of smart street light systems is that lights turn on when needed and light turn off when not needed. Moreover, the smart street light system in this paper behaves like usual street lights that turn on all night. The ideal behavior of the smart street light system is that no one finds turn-off of street lights at night. Whenever someone see street lights, they turn on and whenever no one see street lights, they turn off. The smart street light system consists of LED lights, brightness sensors, motion sensors and short-distance communication networks. The lights turn on before pedestrians and vehicles come and turn off or reduce brightness when there is no one. It will be difficult for pedestrians and drivers of vehicles to distinguish our smart street lights and the conventional street lights because our street lights all turn on before they come.
On the Consistency of Top-k Surrogate Losses
(2019)

The top-k error is often employed to evaluate performance for challenging classification tasks in computer vision as it is designed to compensate for ambiguity in ground truth labels. This practical success motivates our theoretical analysis of consistent top-k classification. To this end, we define top-k calibration as a necessary and sufficient condition for consistency, for bounded below loss functions. Unlike prior work, our analysis of top-k calibration handles non-uniqueness of the predictor scores, and extends calibration to consistency – providing a theoretically sound basis for analysis of this topic. Based on the topk calibration analysis, we propose a rich class of top-k calibrated Bregman divergence surrogates. Our analysis continues by showing previously proposed hinge-like top-k surrogate losses are not top-k calibrated and thus inconsistent. On the other hand, we propose two new hinge-like losses, one which is similarly inconsistent, and one which is consistent. Our empirical results highlight theoretical claims, confirming our analysis of the consistency of these losses.
Genetic studies of human diversity in East Asia.
(2007)

East Asia is one of the most important regions for studying evolution and genetic diversity of human populations. Recognizing the relevance of characterizing the genetic diversity and structure of East Asian populations for understanding their genetic history and designing and interpreting genetic studies of human diseases, in recent years researchers in China have made substantial efforts to collect samples and generate data especially for markers on Y chromosomes and mtDNA. The hallmark of these efforts is the discovery and confirmation of consistent distinction between northern and southern East Asian populations at genetic markers across the genome. With the confirmation of an African origin for East Asian populations and the observation of a dominating impact of the gene flow entering East Asia from the south in early human settlement, interpretation of the north-south division in this context poses the challenge to the field. Other areas of interest that have been studied include the gene flow between East Asia and its neighbouring regions (i.e. Central Asia, the Sub-continent, America and the Pacific Islands), the origin of Sino-Tibetan populations and expansion of the Chinese.
A Secured System for Information Hiding in Image Steganography using Genetic Algorithm and Cryptography
(2016)

Encryption is used to securely communicate data in open networks. Each type of data has its own structures; therefore according to the data the different techniques should be used to defend confidential data. Among them digital images are also very popular to carry confidential information in untrusted networks. . For pleasing the defense of data hiding and communication over network, the proposed system uses cryptographic algorithm along with Steganography. In the proposed system, the file which user want to make secure is firstly compressed to shrink in size and then the compressed data is altered into cipher text by using AES cryptographic algorithm and then the encrypted data is concealed in the image. In order to hide the information over the image in complex manner the genetic algorithm based technique is implemented which is used to evaluate the valuable pixels where the data can be hide in a secure manner. In addition of that, for hiding the information in images, the LSB (least significant bits) based steganographic method is used after the selection of eligible pixels. The implementation of the anticipated technique is performed using JAVA technology and for performance evaluation the time and space complexity is computed. In addition of that a
Generating multi-fingered robotic grasps via deep learning
(2015)

This paper presents a deep learning architecture for detecting the palm and fingertip positions of stable grasps directly from partial object views. The architecture is trained using RGBD image patches of fingertip and palm positions from grasps computed on complete object models using a grasping simulator. At runtime, the architecture is able to estimate grasp quality metrics without the need to explicitly calculate the given metric. This ability is useful as the exact calculation of these quality functions is impossible from an incomplete view of a novel object without any tactile feedback. This architecture for grasp quality prediction provides a framework for generalizing grasp experience from known to novel objects.
Training Products of Experts by Minimizing Contrastive Divergence
(2002)

It is possible to combine multiple latent-variable models of the same data by multiplying their probability distributions together and then renormalizing. This way of combining individual expert models makes it hard to generate samples from the combined model but easy to infer the values of the latent variables of each expert, because the combination rule ensures that the latent variables of different experts are conditionally independent when given the data. A product of experts (PoE) is therefore an interesting candidate for a perceptual system in which rapid inference is vital and generation is unnecessary. Training a PoE by maximizing the likelihood of the data is difficult because it is hard even to approximate the derivatives of the renormalization term in the combination rule. Fortunately, a PoE can be trained using a different objective function called contrastive divergence whose derivatives with regard to the parameters can be approximated accurately and efficiently. Examples are presented of contrastive divergence learning using several types of expert on several types of data.
Artificial General Intelligence
(2018)

Against the backdrop of increasing progresses in AI research paired with a rise of AI applications in decision-making processes, security-critical domains as well as in ethically relevant frames, a largescale debate on possible safety measures encompassing corresponding long-term and short-term issues has emerged across different disciplines. One pertinent topic in this context which has been addressed by various AI Safety researchers is e.g. the AI alignment problem for which no final consensus has been achieved yet. In this paper, we present a multidisciplinary toolkit of AI Safety strategies combining considerations from AI and Systems Engineering as well as from Cognitive Science with a security mindset as often relevant in Cybersecurity. We elaborate on how AGI “Self-awareness” could complement different AI Safety measures in a framework extended by a jointly performed Human Enhancement procedure. Our analysis suggests that this hybrid framework could contribute to undertake the AI alignment problem from a new holistic perspective through security-building synergetic effects emerging thereof and could help to increase the odds of a possible safe future transition towards superintelligent systems.
Spectrum Sensing Methods and Dynamic Spectrum Sharing in Cognitive Radio Networks: A Survey
(2011)

Dynamic spectrum access and cognitive radio (CR) are emerging technologies to utilize the scarce frequency spectrum in an efficient and opportunistic manner. They have been receiving tremendous amount of well-deserved attention in the literature. One of the challenging problems in dynamic spectrum access for cognitive radios is to sense the spectrum in wide band regime of heterogeneous networks to identify the spectrum opportunities. Our main goal in this paper is to present the state-of-the-art research results in signal processing techniques for spectrum sensing in CR network to make automatic and wise decision by unlicensed secondary users to adapt their transmission parameters according to their operating RF environment and respect the primary users. Specifically, we present dynamic spectrum sharing models for spectrum access and signal processing techniques for spectrum sensing by categorizing them into five basic groups. We also present the comparison of different signal processing techniques from different perspectives including networking metrics for the evaluation of the methods e.g. how accurate and how fast can sense spectrum utilization given the various methods and how this will affect networking functionality, and pros and cons of all the methods in the context of networking systems. We also present the status, research challenges and perspectives of signal processing/detection techniques for spectrum sensing in cognitive radio networks.
Signal Processing in Cognitive Radio
(2009)

Cognitive radio allows for usage of licensed frequency bands by unlicensed users. However, these unlicensed (cognitive) users need to monitor the spectrum continuously to avoid possible interference with the licensed (primary) users. Apart from this, cognitive radio is expected to learn from its surroundings and perform functions that best serve its users. Such an adaptive technology naturally presents unique signal-processing challenges. In this paper, we describe the fundamental signal-processing aspects involved in developing a fully functional cognitive radio network, including spectrum sensing and spectrum sculpting.
Cognitive radio: brain-empowered wireless communications
(2005)

Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a software-defined radio, is defined as an intelligent wireless communication system that is aware of its environment and uses the methodology of understanding-by-building to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: /spl middot/ highly reliable communication whenever and wherever needed; /spl middot/ efficient utilization of the radio spectrum. Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks. 1) Radio-scene analysis. 2) Channel-state estimation and predictive modeling. 3) Transmit-power control and dynamic spectrum management. This work also discusses the emergent behavior of cognitive radio.
Cooperative Sensing among Cognitive Radios
(2006)

Cognitive Radios have been advanced as a technology for the opportunistic use of under-utilized spectrum since they are able to sense the spectrum and use frequency bands if no Primary user is detected. However, the required sensitivity is very demanding since any individual radio might face a deep fade. We propose light-weight cooperation in sensing based on hard decisions to mitigate the sensitivity requirements on individual radios. We show that the "link budget" that system designers have to reserve for fading is a significant function of the required probability of detection. Even a few cooperating users (~10-20) facing independent fades are enough to achieve practical threshold levels by drastically reducing individual detection requirements. Hard decisions perform almost as well as soft decisions in achieving these gains. Cooperative gains in a environment where shadowing is correlated, is limited by the cooperation footprint (area in which users cooperate). In essence, a few independent users are more robust than many correlated users. Unfortunately, cooperative gain is very sensitive to adversarial/failing Cognitive Radios. Radios that fail in a known way (always report the presence/absence of a Primary user) can be compensated for by censoring them. On the other hand, radios that fail in unmodeled ways or may be malicious, introduce a bound on achievable sensitivity reductions. As a rule of thumb, if we believe that 1/N users can fail in an unknown way, then the cooperation gains are limited to what is possible with N trusted users.
An intrusion detection system against malicious attacks on the communication network of driverless cars
(2015)

Vehicular ad hoc networking (VANET) have become a significant technology in the current years because of the emerging generation of self-driving cars such as Google driverless cars. VANET have more vulnerabilities compared to other networks such as wired networks, because these networks are an autonomous collection of mobile vehicles and there is no fixed security infrastructure, no high dynamic topology and the open wireless medium makes them more vulnerable to attacks. It is important to design new approaches and mechanisms to rise the security these networks and protect them from attacks. In this paper, we design an intrusion detection mechanism for the VANETs using Artificial Neural Networks (ANNs) to detect Denial of Service (DoS) attacks. The main role of IDS is to detect the attack using a data generated from the network behavior such as a trace file. The IDSs use the features extracted from the trace file as auditable data. In this paper, we propose anomaly and misuse detection to detect the malicious attack.